YOLO -V2.0 Implementation¶
- Implementated from scratch using tensorflow
- Tested for COCO dataset
In [1]:
#!pip install tensorflow==1.12.0
In [2]:
# Initialization
colab_run=False
anc_box= True
_grid_offset=True
train=False
test_mode=2
data_save=True
root='D:/'
train_dir='COCO/train2017/train2017'
val_dir='COCO/val2017/val2017/'
test_dir='COCO/test2017/test2017/'
if colab_run==True:
!pip install pydrive
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials
auth.authenticate_user()
gauth = GoogleAuth()
gauth.credentials = GoogleCredentials.get_application_default()
drive = GoogleDrive(gauth)
import os, cv2
from google.colab import drive
drive.mount('/content/drive/')
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.layers import Reshape, Activation, Conv2D, Input, MaxPooling2D, BatchNormalization, Flatten, Dense, Lambda
from tensorflow.keras.layers import LeakyReLU
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard
from tensorflow.keras.optimizers import SGD, Adam, RMSprop
from tensorflow.keras.layers import concatenate
from tensorflow.keras.callbacks import ModelCheckpoint
from matplotlib import pyplot as plt
import tensorflow.keras.backend as K
import tensorflow as tf
import numpy as np
import pickle
import os, cv2
#os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
#os.environ["CUDA_VISIBLE_DEVICES"] = ""
# %matplotlib inline
Configure Block¶
Model Building Block¶
In [3]:
LABELS=['person', 'bird', 'cat', 'cow', 'dog', 'horse', 'sheep','aeroplane', 'bicycle',
'boat', 'bus', 'car', 'motorbike', 'train', 'bottle', 'chair','diningtable',
'pottedplant', 'sofa', 'tvmonitor']
f=open('coco.txt','r')
LABELS=[]
for line in f:
line=line.split(':')
LABELS.append(str(line[1][1:-1]))
f=open('D:/COCO/labels_coco','rb')
_LABELS=pickle.load(f)
f.close()
LABELS=list(_LABELS.values())
IMAGE_H, IMAGE_W = 608, 608
GRID_H, GRID_W = 19 , 19
BOX = 5
CLASS = len(LABELS)
CLASS_WEIGHTS = np.ones(CLASS, dtype='float32')
OBJ_THRESHOLD = 0.3#0.5
NMS_THRESHOLD = 0.3#0.45
ANCHORS = [0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828]
#ANCHORS = [1.3221, 1.73145, 3.19275, 4.00944, 5.05587, 8.09892, 9.47112, 4.84053, 11.2364, 10.0071]
#ANCHORS = [ 4.469053,2.148582,10.548851,5.381520,11.420664,9.961033,6.517299,3.699693,2.469196,1.599054]
#ANCHORS=[13,13]
#NO_OBJECT_SCALE = 1.0
#OBJECT_SCALE = 5.0
#COORD_SCALE = 1.0
#CLASS_SCALE = 1.0
BATCH_SIZE = 10
WARM_UP_BATCHES = 0
TRUE_BOX_BUFFER = 50
lambda_coord=5.0
lambda_noobj=0.5
lr=1e-5
epochs=60
optimizer = Adam(lr=lr, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
#optimizer = SGD(lr=1e-4, decay=0.0005, momentum=0.9)
#optimizer = RMSprop(lr=1e-4, rho=0.9, epsilon=1e-08, decay=0.0)
exp_name='v-2.1/'
root='D:/'
path_wts=root+'yolov2.weights'
path_wts_final=root+exp_name+'best.hdf5'
if not os.path.exists(root+exp_name):
os.mkdir(root+exp_name)
In [4]:
# the function to implement the orgnization layer (thanks to github.com/allanzelener/YAD2K)
def space_to_depth_x2(x):
return tf.space_to_depth(x, block_size=2)
def build_model():
input_image = Input(shape=(IMAGE_H, IMAGE_W, 3))
true_boxes = Input(shape=(1, 1, 1, TRUE_BOX_BUFFER , 4))
# Layer 1
x = Conv2D(32, (3,3), strides=(1,1), padding='same', name='conv_1', use_bias=False)(input_image)
x = BatchNormalization(name='norm_1')(x)
x = LeakyReLU(alpha=0.1)(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
# Layer 2
x = Conv2D(64, (3,3), strides=(1,1), padding='same', name='conv_2', use_bias=False)(x)
x = BatchNormalization(name='norm_2')(x)
x = LeakyReLU(alpha=0.1)(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
# Layer 3
x = Conv2D(128, (3,3), strides=(1,1), padding='same', name='conv_3', use_bias=False)(x)
x = BatchNormalization(name='norm_3')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 4
x = Conv2D(64, (1,1), strides=(1,1), padding='same', name='conv_4', use_bias=False)(x)
x = BatchNormalization(name='norm_4')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 5
x = Conv2D(128, (3,3), strides=(1,1), padding='same', name='conv_5', use_bias=False)(x)
x = BatchNormalization(name='norm_5')(x)
x = LeakyReLU(alpha=0.1)(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
# Layer 6
x = Conv2D(256, (3,3), strides=(1,1), padding='same', name='conv_6', use_bias=False)(x)
x = BatchNormalization(name='norm_6')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 7
x = Conv2D(128, (1,1), strides=(1,1), padding='same', name='conv_7', use_bias=False)(x)
x = BatchNormalization(name='norm_7')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 8
x = Conv2D(256, (3,3), strides=(1,1), padding='same', name='conv_8', use_bias=False)(x)
x = BatchNormalization(name='norm_8')(x)
x = LeakyReLU(alpha=0.1)(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
# Layer 9
x = Conv2D(512, (3,3), strides=(1,1), padding='same', name='conv_9', use_bias=False)(x)
x = BatchNormalization(name='norm_9')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 10
x = Conv2D(256, (1,1), strides=(1,1), padding='same', name='conv_10', use_bias=False)(x)
x = BatchNormalization(name='norm_10')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 11
x = Conv2D(512, (3,3), strides=(1,1), padding='same', name='conv_11', use_bias=False)(x)
x = BatchNormalization(name='norm_11')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 12
x = Conv2D(256, (1,1), strides=(1,1), padding='same', name='conv_12', use_bias=False)(x)
x = BatchNormalization(name='norm_12')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 13
x = Conv2D(512, (3,3), strides=(1,1), padding='same', name='conv_13', use_bias=False)(x)
x = BatchNormalization(name='norm_13')(x)
x = LeakyReLU(alpha=0.1)(x)
skip_connection = x
x = MaxPooling2D(pool_size=(2, 2))(x)
# Layer 14
x = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_14', use_bias=False)(x)
x = BatchNormalization(name='norm_14')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 15
x = Conv2D(512, (1,1), strides=(1,1), padding='same', name='conv_15', use_bias=False)(x)
x = BatchNormalization(name='norm_15')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 16
x = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_16', use_bias=False)(x)
x = BatchNormalization(name='norm_16')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 17
x = Conv2D(512, (1,1), strides=(1,1), padding='same', name='conv_17', use_bias=False)(x)
x = BatchNormalization(name='norm_17')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 18
x = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_18', use_bias=False)(x)
x = BatchNormalization(name='norm_18')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 19
x = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_19', use_bias=False)(x)
x = BatchNormalization(name='norm_19')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 20
x = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_20', use_bias=False)(x)
x = BatchNormalization(name='norm_20')(x)
x = LeakyReLU(alpha=0.1)(x)
# Layer 21
skip_connection = Conv2D(64, (1,1), strides=(1,1), padding='same', name='conv_21', use_bias=False)(skip_connection)
skip_connection = BatchNormalization(name='norm_21')(skip_connection)
skip_connection = LeakyReLU(alpha=0.1)(skip_connection)
skip_connection = Lambda(space_to_depth_x2)(skip_connection)
x = concatenate([skip_connection, x])
# Layer 22
x = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_22', use_bias=False)(x)
x = BatchNormalization(name='norm_22')(x)
x = LeakyReLU(alpha=0.1)(x)
if anc_box==True:
# Layer 23
x = Conv2D(BOX*(4 + 1 + CLASS), (1,1), strides=(1,1), padding='same', name='conv_23')(x)
output = Reshape((GRID_H, GRID_W,BOX,4 + 1 + CLASS))(x)
else :
# Layer 23
x = Conv2D((4 + 1 + CLASS), (1,1), strides=(1,1), padding='same', name='conv_23')(x)
output = Reshape((GRID_H, GRID_W,4 + 1 + CLASS))(x)
# small hack to allow true_boxes to be registered when Keras build the model
# for more information: https://github.com/fchollet/keras/issues/2790
#output = Lambda(lambda args: args[0])([output, true_boxes])#Change :Hasib
#model = Model([input_image, true_boxes], output)#Change :Hasib
model = Model(input_image, output)
return model
#model.load_weights('/content/drive/My Drive/Data/yolo_net_ep500_act.h5')
class WeightReader:
def __init__(self, weight_file):
self.offset = 4
self.all_weights = np.fromfile(weight_file, dtype='float32')
def read_bytes(self, size):
self.offset = self.offset + size
return self.all_weights[self.offset-size:self.offset]
def reset(self):
self.offset = 4
def load_weights(model,path_wst):
wt_path = path_wts
weight_reader = WeightReader(wt_path)
weight_reader.reset()
nb_conv = 23
for i in range(1, nb_conv+1):
conv_layer = model.get_layer('conv_' + str(i))
if i < nb_conv:
norm_layer = model.get_layer('norm_' + str(i))
size = np.prod(norm_layer.get_weights()[0].shape)
beta = weight_reader.read_bytes(size)
gamma = weight_reader.read_bytes(size)
mean = weight_reader.read_bytes(size)
var = weight_reader.read_bytes(size)
weights = norm_layer.set_weights([gamma, beta, mean, var])
if len(conv_layer.get_weights()) > 1:
bias = weight_reader.read_bytes(np.prod(conv_layer.get_weights()[1].shape))
kernel = weight_reader.read_bytes(np.prod(conv_layer.get_weights()[0].shape))
kernel = kernel.reshape(list(reversed(conv_layer.get_weights()[0].shape)))
kernel = kernel.transpose([2,3,1,0])
conv_layer.set_weights([kernel, bias])
else:
kernel = weight_reader.read_bytes(np.prod(conv_layer.get_weights()[0].shape))
kernel = kernel.reshape(list(reversed(conv_layer.get_weights()[0].shape)))
kernel = kernel.transpose([2,3,1,0])
conv_layer.set_weights([kernel])
return model
def yolo_loss_1(y_true, y_pred):
loss=0
### adjust w and h
obj_mask_ex= tf.expand_dims(y_true[..., 4], axis=-1)
obj_mask= y_true[..., 4]
#conf_obj_mask=y_true[...,4]
noobj_mask=np.abs(y_true[...,4]-1)
if anc_box==True and _grid_offset==True:
_x = tf.to_float(tf.reshape(tf.tile(tf.range(GRID_W), [GRID_H]), (1, GRID_H, GRID_W, 1, 1)))#1,13,13,1,1
_y = tf.transpose(_x, (0,2,1,3,4))#1,13,13,1,1
_grid = tf.tile(tf.concat([_x,_y], -1), [BATCH_SIZE, 1, 1, 5, 1])#10,13,13,5,1
pred_xy = tf.sigmoid(y_pred[..., :2]) + _grid
pred_wh = tf.exp(y_pred[..., 2:4]) * np.reshape(ANCHORS, [1,1,1,BOX,2])
if anc_box==False and _grid_offset==True:
_x = tf.to_float(tf.reshape(tf.tile(tf.range(GRID_W), [GRID_H]), (1, GRID_H, GRID_W, 1)))#1,13,13,1,1
_y = tf.transpose(_x, (0,2,1,3))#1,13,13,1
_grid = tf.tile(tf.concat([_x,_y], -1), [BATCH_SIZE, 1, 1, 1])#10,13,13,1
pred_xy = tf.sigmoid(y_pred[..., :2]) + _grid
pred_wh = y_pred[..., 2:4]# * np.reshape(ANCHORS, [1,1,1,BOX,2])
#pred_wh = tf.exp(y_pred[..., 2:4]) * np.reshape(ANCHORS, [1,1,1,BOX,2])
if _grid_offset==False:
pred_xy=y_pred[...,0:2] #+ cell_grid # if cell_grid Batch_Gen center_x -=grid_x
pred_wh=y_pred[...,2:4]
true_xy=y_true[...,0:2]
true_wh=y_true[...,2:4]
pred_conf=y_pred[...,4]
### adjust confidence
true_wh_half = true_wh / 2.
true_mins = tf.subtract(true_xy,true_wh_half)
true_maxes = tf.add(true_xy,true_wh_half)
pred_wh_half = pred_wh / 2.
pred_mins = tf.subtract(pred_xy,pred_wh_half)
pred_maxes = tf.add(pred_xy,pred_wh_half)
intersect_mins = tf.maximum(pred_mins, true_mins)
intersect_maxes = tf.minimum(pred_maxes, true_maxes)
intersect_wh = tf.maximum(intersect_maxes - intersect_mins, 0.)
intersect_areas = tf.multiply(intersect_wh[..., 0] , intersect_wh[..., 1])
true_areas = tf.multiply(true_wh[..., 0] , true_wh[..., 1])
pred_areas = tf.multiply(pred_wh[..., 0] , pred_wh[..., 1])
union_areas =tf.subtract(tf.add(pred_areas,true_areas),intersect_areas)
intersect_areas=tf.add(intersect_areas,1)
union_areas=tf.add(union_areas,1)
iou_scores = tf.truediv(intersect_areas, union_areas)
true_box_class = tf.argmax(y_true[..., 5:], -1)
pred_box_class=y_pred[..., 4]
pred_box_class = y_pred[..., 5:]
class_mask = y_true[..., 4] * tf.gather(CLASS_WEIGHTS, true_box_class)
#class_mask = y_true[..., 4] * tf.to_float(true_box_class)
nb_class_box = tf.reduce_sum(tf.to_float(class_mask > 0.0))
true_conf =tf.multiply( iou_scores,y_true[..., 4])
loss_bb=tf.subtract(true_xy,pred_xy)
loss_bb=tf.square(loss_bb)
loss_bb=tf.multiply(loss_bb,obj_mask_ex)
loss_bb=tf.reduce_sum(loss_bb)
pred_wh_abs=tf.abs(pred_wh)
pred_wh_abs=tf.where(tf.equal(pred_wh_abs,0),tf.ones_like(pred_wh_abs),pred_wh_abs)
pred_wh_sign=tf.truediv(pred_wh,pred_wh_abs)
loss_wh=tf.subtract(tf.sqrt(true_wh),tf.multiply(pred_wh_sign,tf.sqrt(pred_wh_abs)))
loss_wh=tf.square(loss_wh)
loss_wh=tf.multiply(loss_wh,obj_mask_ex)
loss_wh=tf.reduce_sum(loss_wh)
loss_conf=tf.subtract(true_conf,pred_conf)
loss_conf=tf.square(loss_conf)
loss_conf=tf.multiply(loss_conf,obj_mask)
loss_conf=tf.reduce_sum(loss_conf)
loss_noobj_conf=tf.subtract(true_conf,pred_conf)
loss_noobj_conf=tf.square(loss_noobj_conf)
loss_noobj_conf=tf.multiply(loss_noobj_conf,noobj_mask)
loss_noobj_conf=tf.reduce_sum(loss_noobj_conf)
loss_class = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=true_box_class, logits=pred_box_class)
loss_class = tf.reduce_sum(loss_class * class_mask) / (nb_class_box + 1e-6)
loss=lambda_coord*loss_bb+lambda_coord*loss_wh+loss_conf+lambda_noobj*loss_noobj_conf+loss_class
return loss
Data Ready Block¶
In [5]:
if colab_run==True :data_dir='/content/drive/My Drive/CNN_Basic/'
else: data_dir=''
from xml.etree import ElementTree as ET
def read_content(xml_file,_dir):
objs=[]
tree = ET.parse(xml_file)
root = tree.getroot()
list_with_all_boxes = []
filename = root.find('filename').text
size=root.find('size')
img_h=int(size.find('height').text)
img_w=int(size.find('width').text)
for boxes in root.iter('object'):
name = boxes.find('name').text
ymin, xmin, ymax, xmax = None, None, None, None
for box in boxes.findall("bndbox"):
ymin = int(box.find("ymin").text)
xmin = int(box.find("xmin").text)
ymax = int(box.find("ymax").text)
xmax = int(box.find("xmax").text)
wf=IMAGE_W/img_w
hf=IMAGE_H/img_h
obj={'name':name,
'xmin':xmin*wf,
'ymin':ymin*hf,
'xmax':xmax*wf,
'ymax':ymax*hf}
objs.append(obj)
out={
'filename':_dir+'JPEGImages/'+filename,
'height':img_h,
'width':img_w,
'object':objs
}
return filename, list_with_all_boxes,out
def IOU(bboxes1, bboxes2):
#import pdb;pdb.set_trace()
x1_min, y1_min, x1_max, y1_max = list(bboxes1)
x2_min, y2_min, x2_max, y2_max = list(bboxes2)
xA = np.maximum(x1_min, x2_min)
yA = np.maximum(y1_min, y2_min)
xB = np.minimum(x1_max, x2_max)
yB = np.minimum(y1_max, y2_max)
interArea = np.maximum((xB - xA ), 0) * np.maximum((yB - yA ), 0)
boxAArea = (x1_max - x1_min ) * (y1_max - y1_min )
boxBArea = (x2_max - x2_min ) * (y2_max - y2_min )
iou = interArea / (boxAArea + boxBArea - interArea)
return iou
def Batch_Gen(all_data,no_of_batch):
while(True):
N=len(all_data)
_batch_size=N//no_of_batch
for _ind in range(no_of_batch):
batch=all_data[_ind*_batch_size:(_ind+1)*_batch_size]
n=len(batch)
x_batch = np.zeros((n,IMAGE_H, IMAGE_W,3),dtype=np.float32) # input images
if anc_box==True:
y_batch = np.zeros((n, GRID_H, GRID_W,BOX,4+1+len(LABELS)),dtype=np.float) # desired network output
else :
y_batch = np.zeros((n, GRID_H, GRID_W,4+1+len(LABELS)),dtype=np.float) # desired network output
instance_count=0
for sample in batch:
image_name = sample['filename']
img = cv2.imread(root+train_dir+image_name)
img = cv2.resize(img, (IMAGE_H,IMAGE_W))
img = img[:,:,::-1]
img_w=sample['height']
img_h=sample['width']
all_objs = sample['object']
# construct output from object's x, y, w, h
true_box_index = 0
anchors = [[0, 0, ANCHORS[2*i], ANCHORS[2*i+1]] for i in range(int(len(ANCHORS)//2))]
for obj in all_objs:
if obj['xmax'] > obj['xmin'] and obj['ymax'] > obj['ymin'] and obj['name'] in LABELS:
center_x = .5*(obj['xmin'] + obj['xmax'])
center_x = center_x*GRID_W
center_y = .5*(obj['ymin'] + obj['ymax'])
center_y=center_y*GRID_H
grid_x = int(np.floor(center_x))
grid_y = int(np.floor(center_y))
if _grid_offset==False:
center_x-=grid_x
center_y-=grid_y
#center_x-=grid_x
#center_y-=grid_y
if grid_x < GRID_W and grid_y < GRID_H:
obj_indx = LABELS.index(obj['name'])
center_w = (obj['xmax'] - obj['xmin'])*GRID_W #/ (float(self.config['IMAGE_W'])# / self.config['GRID_W']) # unit: grid cell
center_h = (obj['ymax'] - obj['ymin'])*GRID_H #/ (float(self.config['IMAGE_H'])# / self.config['GRID_H']) # unit: grid cell
center_w=center_w
center_h=center_h
box = [center_x, center_y, center_w, center_h]
# find the anchor that best predicts this box#Change :Hasib
best_anchor = -1
max_iou = -1
shifted_box = [0, 0, center_w, center_h]
for i in range(len(anchors)):
anchor = anchors[i]
iou = IOU(shifted_box, anchor)
if max_iou < iou:
best_anchor = i
max_iou = iou
# assign ground truth x, y, w, h, confidence and class probs to y_batch
if anc_box==True:
y_batch[instance_count, grid_y, grid_x, best_anchor,0:4] = box
y_batch[instance_count, grid_y, grid_x, best_anchor,4 ] = 1.
y_batch[instance_count, grid_y, grid_x, best_anchor,5+obj_indx] = 1
else :
y_batch[instance_count, grid_y, grid_x,0:4] = box
y_batch[instance_count, grid_y, grid_x,4 ] = 1.
y_batch[instance_count, grid_y, grid_x,5+obj_indx] = 1
# assign the true box to b_batch
#b_batch[instance_count, 0, 0, 0, true_box_index] = box#Change: Hasib
#true_box_index += 1
#true_box_index = true_box_index % self.config['TRUE_BOX_BUFFER']
# assign input image to x_batch
x_batch[instance_count] = img/255
# increase instance counter in current batch
instance_count += 1
#print(' new batch created', idx)
yield (x_batch, y_batch)
Reading Data¶
In [6]:
import glob
import cv2
import pandas as pd
import pickle
f=open(root+'COCO/train_coco_dfs','rb')
train_data=pickle.load(f)
f.close()
f=open(root+'COCO/val_coco_dfs','rb')
valid_data=pickle.load(f)
f.close()
f=open(root+'COCO/test_coco_dfs','rb')
test_data=pickle.load(f)
f.close()
In [7]:
if train==True or train==False:
#train_imgs=all_data[:n_train]
N=len(train_data)
n_train=len(train_data)
n_valid=len(valid_data)
train_batch=[]
valid_batch=[]
no_of_tr_batch=int(np.floor(n_train/BATCH_SIZE))
train_batch=Batch_Gen(train_data,no_of_tr_batch)
no_of_val_batch=int(np.floor(n_valid/BATCH_SIZE))
valid_batch=Batch_Gen(valid_data,no_of_val_batch)
In [8]:
name=[]
class_wt=pd.Series([0]*len(LABELS),index=LABELS)
for sample in train_data:
names=[obj['name'] for obj in sample['object']]
for label in names:
class_wt[label]+=1
class_weights=class_wt.sum()-class_wt#.values
class_weights=class_weights/class_weights.max()
#CLASS_WEIGHTS=np.array(class_weights.values,dtype=np.float32)
In [9]:
for i in range(30,60):
image_h=(valid_data[i]['height'])
image_w=(valid_data[i]['width'])
img=cv2.imread(root+val_dir+valid_data[i]['filename'])
for obj in valid_data[i]['object']:
print(obj['name'],valid_data[i]['filename'])
xmin,ymin,xmax,ymax=list(obj.values())[1:]
cv2.rectangle(img, (int(xmin*image_w),int(ymin*image_h)), (int(xmax*image_w),int(ymax*image_h)), (0,255,0), 3)
plt.imshow(img)
plt.show()
In [10]:
image_h=(test_data[i]['height']/416)
test_data[i]['height']/416
Out[10]:
data_check=True if data_check==True: sampleX,sampleY=next(train_batch) plt.imshow(sampleX[3]) plt.show() img=sampleY[3][...,3,11+5] plt.imshow(img) plt.show() for i in range(4,5): print('index',i) plt.imshow(sampleY[3][...,3,i]*10) plt.show()
Train Block¶
In [11]:
model=build_model()
model.summary()
In [13]:
def train_model(model):
#filepath="D:/"+exp_name+"weights-improvement-{epoch:02d}-{val_loss:.2f}.hdf5"
filepath=root+exp_name+"best.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
model.compile(loss=yolo_loss_1, optimizer=optimizer,metrics=['accuracy'])
if anc_box==True:
model.compile(loss=yolo_loss_1, optimizer=optimizer,metrics=['accuracy'])
history=model.fit_generator(train_batch,
steps_per_epoch=no_of_tr_batch,
epochs=epochs,
callbacks=callbacks_list,
validation_data=valid_batch,
validation_steps=no_of_val_batch)
return model,history
In [14]:
if train==True:
model=load_weights(model,path_wts)
for layer in model.layers[:-31]:
layer.trainable=False
model,history=train_model(model)
plt.plot(history.history['acc'])
plt.plot(history.history['loss'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
# summarize history for loss
plt.plot(range(5,epochs),history.history['loss'][5:])
plt.plot(range(5,epochs),history.history['val_loss'][5:])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train_loss', 'validation_loss'], loc='upper left')
plt.savefig(root+exp_name+'yolo-v1_exp.png')
plt.show()
f=open(root+exp_name+'history:_ep_70_v-1.0_exp','wb')
pickle.dump([history.history['loss'],history.history['val_loss']],f)
f.close()
val_loss=np.array(history.history['val_loss'])
best_epoch=np.argmin(val_loss)
print(best_epoch,val_loss[best_epoch])
if test_mode==1:
model.load_weights(path_wts_final)
if test_mode==2:
model=load_weights(model,path_wts)
Predicion Block¶
In [15]:
def _softmax(x, axis=-1, t=-100.):
x = x - np.max(x)
if np.min(x) < t:
x = x/np.min(x)*t
e_x = np.exp(x)
return e_x / e_x.sum(axis, keepdims=True)
def _sigmoid(x):
return 1. / (1. + np.exp(-x))
def nmax_supp(boxes):
df=pd.DataFrame(boxes,columns=['x','y','w','h','conf','_class'])
sdf=df.sort_values('conf',ascending=False).reset_index(drop=True)
#print(sdf)
for ind1 in range(len(sdf)-1):
box1=sdf.loc[ind1,['x','y','w','h']].values
b1_class=sdf.loc[ind1,['_class']].values
for ind2 in range(ind1+1,len(sdf)):
b2_class=sdf.loc[ind2,['_class']].values
b2_conf=sdf.loc[ind2,['conf']].values
if b2_class==b1_class and b2_conf>0.0:
box2=sdf.loc[ind2,['x','y','w','h']].values
if b1_class==b2_class and IOU(box1,box2)>=0.1:
sdf.loc[ind2,'conf']=0.0
ndf=sdf[sdf.conf>0.0]
return list(ndf.values)#[:len(ndf.values)//2]
def decode_netout_anc(netout, anchors, nb_class, obj_threshold=0.3, nms_threshold=0.3):
#grid_h, grid_w, nb_box = netout.shape[:3]
grid_h, grid_w = netout.shape[:2]
boxes = []
# decode the output by the network
netout[..., 4] = _sigmoid(netout[..., 4])
netout[..., 5:] = netout[..., 4][..., np.newaxis] * _softmax(netout[..., 5:])
netout[..., 5:] *= netout[..., 5:] > obj_threshold
for row in range(grid_h):
for col in range(grid_w):
for b in range(BOX):
# from 4th element onwards are confidence and class classes
classes = netout[row,col,b,5:]
#classes = netout[row,col,5:]
confidence = netout[row,col,b,4]
if np.sum(classes) > 0:
x, y, w, h = netout[row,col,b,:4]
x = (col + _sigmoid(x)) / grid_w # center position, unit: image width
y = (row + _sigmoid(y)) / grid_h # center position, unit: image height
w = ANCHORS[2 * b + 0] * np.exp(w) / grid_w # unit: image width
h = ANCHORS[2 * b + 1] * np.exp(h) / grid_h # unit: image height
#print(x,y,w,h)
classes=np.argmax(classes)
box = (x-w/2, y-h/2, x+w/2, y+h/2, confidence, classes)
box = (x-w/2, y-h/2, x+w/2, y+h/2, confidence, classes)
boxes.append(box)
f_boxes=nmax_supp(boxes)
return f_boxes
def decode_netout_1(netout, anchors, nb_class, obj_threshold=0.3, nms_threshold=0.3):
#grid_h, grid_w, nb_box = netout.shape[:3]
grid_h, grid_w = netout.shape[:2]
boxes = []
# decode the output by the network
netout[..., 4] = _sigmoid(netout[..., 4])
netout[..., 5:] = netout[..., 4][..., np.newaxis] * _softmax(netout[..., 5:])
netout[..., 5:] *= netout[..., 5:] > obj_threshold
class_prob_log=[0]*CLASS
#boxes=[(0,)*6]*CLASS
boxes=[]
for row in range(grid_h):
for col in range(grid_w):
#for b in range(BOX):
# from 4th element onwards are confidence and class classes
classes = netout[row,col,5:]
#classes = netout[row,col,5:]
confidence = netout[row,col,4]
if np.sum(classes) > 0:
# first 4 elements are x, y, w, and h
#x, y, w, h = netout[row,col,b,:4]
x, y, w, h = netout[row,col,:4]
#print(col,_sigmoid(x-col),row,_sigmoid(y),w,h)
w_ratio=(IMAGE_W/GRID_W)/IMAGE_W
h_ratio=(IMAGE_W/GRID_W)/IMAGE_H
x = (col+_sigmoid(x))*(h_ratio)#x*(32/416) # center position, unit: image width
y = (row+_sigmoid(y))*(w_ratio)#y*(32/416) # center position, unit: image height
w = w*(w_ratio) # unit: image width
h = h*(h_ratio) # unit: image height
#print(x,y,w,h)
class_ind=np.argmax(classes)
#if class_prob_log[class_ind]< classes[class_ind]:
class_prob_log[class_ind]=classes[class_ind]
box = (x-w/2, y-h/2, x+w/2, y+h/2, confidence, class_ind)
#if abs(box[0])<=1 and abs(box[1])<=1 and box[2]<=1 and box[3]<=1 :
# if (box[0])>=0 and (box[1])>=0 and box[2]>=0 and box[3]>=0 :
boxes.append(box)
#boxes[class_ind]=box
f_boxes=nmax_supp(boxes)
return f_boxes#[:5]
def draw_boxes_1(image, boxes, labels,t_lbl=None):
image_h, image_w, _ = image.shape
wf=image_w/IMAGE_W
hf=image_h/IMAGE_H
show_gt=False
if show_gt==True:
for box in t_lbl:
box=list(box.values())
xmin = int(box[1]*wf)
ymin = int(box[2]*hf)
xmax = int(box[3]*wf)
ymax = int(box[4]*hf)
cv2.rectangle(image, (xmin,ymin), (xmax,ymax), (255,0,0), 3)
cv2.putText(image,
box[0] + ' ',
(xmin, ymin - 13),
cv2.FONT_HERSHEY_SIMPLEX,
0.0015 * 400,
(255,0,0), 2)
for box in boxes:
xmin = int(box[0]*image_w)
ymin = int(box[1]*image_h)
xmax = int(box[2]*image_w)
ymax = int(box[3]*image_h)
cv2.rectangle(image, (xmin,ymin), (xmax,ymax), (0,255,0), 3)
cv2.putText(image,
labels[int(box[5])] + ' ' + str(box[4]),
(xmin, ymin - 13),
cv2.FONT_HERSHEY_SIMPLEX,
0.0015 * 400,
(0,255,0), 2)
return image
In [16]:
import json
import pickle
test_on_val =False
test_on_test=True
test_server=True
calc_map=False
In [17]:
error=0
count=0
import pandas as pd
from copy import copy, deepcopy
from tqdm import tqdm,trange
result_df=pd.DataFrame({},columns=['class','iou','correct','True','img','boxx1','boxy1','boxx2','boxy2','conf'])
ind_loc=0
total_true_per_class={}
for _class in LABELS:
total_true_per_class[_class]=0
if not os.path.exists('input/ground-truth'):
os.mkdir('input/ground-truth')
if not os.path.exists('input/detection-results'):
os.mkdir('input/detection-results')
pred_dicts=[]
for i in trange(0,len(test_data)):
count+=1
if test_on_test==True:
sample=test_data[i]
image = cv2.imread('D:/COCO/test2017/test2017/'+sample['filename'])
if test_on_val==True:
sample=valid_data[i]
image = cv2.imread('D:/COCO/val2017/val2017/'+sample['filename'])
image_h,image_w,_=image.shape
input_image = cv2.resize(image, (IMAGE_W, IMAGE_H))
input_image = input_image / 255.
input_image = np.expand_dims(input_image, 0)
netout = model.predict(input_image)
if anc_box==True:
pred_labels = decode_netout_anc(netout[0],
obj_threshold=0.30,
nms_threshold=.90,
anchors=ANCHORS,
nb_class=CLASS)
if test_server==True:
for pred_box in pred_labels:
_x= pred_box[0]
_y= pred_box[1]
_width=(pred_box[2]-_x)
_height=(pred_box[3]-_y)
_class=list(_LABELS.keys())[int(pred_box[5])]
pred={
"image_id": int(sample['id']),
"category_id": int(_class),
"bbox": [round(_x*image_w,2),round(_y*image_h,2),round(_width*image_w,2),round(_height*image_h,2)],
"score": float(pred_box[4]),
}
true_labels=None
pred_dicts.append(pred)
if calc_map==True:
true_labels=sample['object']
true_lb={}
pred_lb={}
for lbl in LABELS:
true_lb[lbl]=[]
pred_lb[lbl]=[]
f=open('input/ground-truth/'+str(i)+'.txt','w')
str_true=''
for lbl in true_labels:
[name,xmin,ymin,xmax,ymax]=list(lbl.values())
true_lb[name].append([(xmin),(ymin), (xmax),(ymax)])#[cx,cy,iw,ih]
try:name=name.split(' ')[0]+'-'+name.split(' ')[1]
except:0
str_true+=str(name)+' '+str(xmin)+' '+str(ymin)+' '+str(xmax)+' '+str(ymax)+'\n'
f.write(str_true)
f.close()
f=open('input/detection-results/'+str(i)+'.txt','w')
str_pred=''
for box in pred_labels:
[xmin,ymin,xmax,ymax,conf,_name]=box
name=LABELS[int(_name)]
pred_lb[name].append([(xmin),(ymin), (xmax),(ymax),conf])#[cx,cy,iw,ih]
try:name=name.split(' ')[0]+'-'+name.split(' ')[1]
except:0
str_pred+=str(name)+' '+str(conf)+' '+str(xmin)+' '+str(ymin)+' '+str(xmax)+' '+str(ymax)+'\n'
f.write(str_pred)
f.close()
for _class in LABELS:
for true_box in true_lb[_class]:
total_true_per_class[_class]+=1
for pred_box in pred_lb[_class]:
_max_iou=0
for _ind,true_box in enumerate(true_lb[_class]):
_iou= IOU(true_box,pred_box[:4])
if _iou>=_max_iou:
_max_iou=_iou
result_df.loc[ind_loc,['class','iou','True','img','boxx1','boxy1','boxx2','boxy2','conf']]=[_class,_max_iou,1.0,i,pred_box[0],pred_box[1],pred_box[2],pred_box[3],pred_box[4]]
if _max_iou>=0.5:
result_df.loc[ind_loc,'correct']=1
else :
result_df.loc[ind_loc,'correct']=0
ind_loc+=1
image = draw_boxes_1(image, pred_labels, LABELS,true_labels)
if i<100:
_image=cv2.resize(image,(200,200))
if i%3==0 :fig,axes=plt.subplots(nrows=1,ncols=3,figsize=(20,5))
axes[i%3].imshow(_image[:,:,::-1])
axes[i%3].set_title(str(i))#+','+str(_ious))
if i%3==3-1 :plt.show()
In [18]:
#result_df#.loc[:50]
In [19]:
len(pred_dicts)
Out[19]:
In [27]:
f=open('test2017_detections_yolo_results.json','w')
json.dump(pred_dicts,f)
f.close()
In [21]:
!python map.py
End¶
In [ ]: